Intermediary emergent content

ABSTRACT

In some implementations, a method includes obtaining an end state of a first content item spanning a first time duration. In some implementations, the end state of the first content item indicates a first state of a synthesized reality (SR) agent at the end of the first time duration. In some implementations, the method includes obtaining an initial state of a second content item spanning a second time duration subsequent the first time duration. In some implementations, the initial state of the second content item indicates a second state of the SR agent at the beginning of the second time duration. In some implementations, the method includes synthesizing an intermediary emergent content item spanning over an intermediary time duration that is between the end of the first time duration and the beginning of the second time duration.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application No.62/737,768, filed on Sep. 27, 2018, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to intermediary emergentcontent.

BACKGROUND

Some devices are capable of generating and presenting synthesizedreality (SR) settings. Some SR settings include virtual settings thatare simulated replacements of physical settings. Some SR settingsinclude augmented settings that are modified versions of physicalsettings. Some devices that present SR settings include mobilecommunication devices such as smartphones, head-mountable displays(HMDs), eyeglasses, heads-up displays (HUDs), and optical projectionsystems. Most previously available devices that present SR settings areineffective at presenting representations of certain objects. Forexample, some previously available devices that present SR settings areunsuitable for presenting representations of objects that are associatedwith an action.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the present disclosure can be understood by those of ordinaryskill in the art, a more detailed description may be had by reference toaspects of some illustrative implementations, some of which are shown inthe accompanying drawings.

FIG. 1 is a block diagram of an example system for generatingintermediary emergent content items in accordance with someimplementations.

FIGS. 2A-2F are diagrams illustrating an example intermediary emergentcontent item in accordance with some implementations.

FIG. 3A is a block diagram of an example system for training anobjective-effectuator engine in accordance with some implementations.

FIG. 3B is a block diagram of a system for generating an intermediaryemergent content item in accordance with some implementations.

FIG. 4A is a block diagram of an example neural network being trained togenerate intermediary emergent content in accordance with someimplementations.

FIG. 4B is a block diagram of an example neural network that generatedintermediary emergent content in accordance with some implementations.

FIGS. 5A-5L are diagrams of an example user interface for generatingintermediary emergent content in accordance with some implementations.

FIGS. 6A-6D are flowchart representations of a method of generatingintermediary emergent content in accordance with some implementations.

FIGS. 7A-7B are flowchart representations of a method of training anobjective-effectuator engine in accordance with some implementations.

FIGS. 8A-8C are flowchart representations of a method of generatingintermediary emergent content item in accordance with someimplementations.

FIG. 9 is a block diagram of a server system that generated intermediaryemergent content in accordance with some implementations.

FIG. 10 is a diagram of an example operating environment in accordancewith some implementations.

In accordance with common practice the various features illustrated inthe drawings may not be drawn to scale. Accordingly, the dimensions ofthe various features may be arbitrarily expanded or reduced for clarity.In addition, some of the drawings may not depict all of the componentsof a given system, method or device. Finally, like reference numeralsmay be used to denote like features throughout the specification andfigures.

SUMMARY

Various implementations disclosed herein include devices, systems, andmethods for synthesizing intermediary emergent content. In variousimplementations, a device includes a non-transitory memory and one ormore processors coupled with the non-transitory memory. In someimplementations, a method includes obtaining an end state of a firstcontent item spanning a first time duration. In some implementations,the end state of the first content item indicates a first state of an SRagent at the end of the first time duration. In some implementations,the method includes obtaining an initial state of a second content itemspanning a second time duration subsequent the first time duration. Insome implementations, the initial state of the second content itemindicates a second state of the SR agent at the beginning of the secondtime duration. In some implementations, the method includes synthesizingan intermediary emergent content item spanning over an intermediary timeduration that is between the end of the first time duration and thebeginning of the second time duration. In some implementations,synthesizing the intermediary emergent content item includes generatinga set of bounded objectives for the SR agent by providing the end stateof the first content item and the initial state of the second contentitem to an emergent content engine. In some implementations, the set ofbounded objectives are bounded by the end state of the first contentitem and the initial state of the second content item. In someimplementations, synthesizing the intermediary emergent content itemincludes generating a set of actions for the SR agent by providing theset of bounded objectives to an SR agent engine. In someimplementations, the first action in the set of actions matches anaction of the SR agent at the end of the first time duration and thelast action in the set of actions matches an action of the SR agent atthe beginning of the second time duration. In some implementations,synthesizing the intermediary emergent content item includes renderingthe intermediary content item for display.

Various implementations disclosed herein include devices, systems, andmethods for training an SR agent engine. In various implementations, adevice includes a non-transitory memory and one or more processorscoupled with the non-transitory memory. In some implementations, amethod includes extracting, from a content item, a set of actionsperformed by an action-performing element in the content item. In someimplementations, the method includes determining, by semantic analysis,a set of objectives for an SR agent based on the set of actions. In someimplementations, an SR representation of the SR agent corresponds to theaction-performing element. In some implementations, the method includestraining, based on the set of objectives, an SR agent engine thatgenerates actions for the SR agent. In some implementations, thetraining is complete when actions generated by the SR agent engine arewithin an acceptability threshold of the set of actions extracted fromthe content item.

Various implementations disclosed herein include devices, systems, andmethods for synthesizing intermediary emergent content. In variousimplementations, a device includes a non-transitory memory and one ormore processors coupled with the non-transitory memory. In someimplementations, a method includes displaying, on the display, a userinterface that includes a first representation of a first content itemspanning a first time duration and a second representation of a secondcontent item spanning a second time duration. In some implementations,the method includes obtaining, via the input device, a user inputcorresponding to a request to generate an intermediary emergent contentitem spanning over an intermediary time duration that is between the endof the first time duration and the beginning of the second timeduration. In some implementations, the method includes in response toobtaining the user input, displaying, on the display, a representationof the intermediary emergent content item between the firstrepresentation of the first content item and the second representationof the second content item. In some implementations, the intermediaryemergent content item is synthesized after the user input is obtained.

In accordance with some implementations, a device includes one or moreprocessors, a non-transitory memory, and one or more programs. In someimplementations, the one or more programs are stored in thenon-transitory memory and are executed by the one or more processors. Insome implementations, the one or more programs include instructions forperforming or causing performance of any of the methods describedherein. In accordance with some implementations, a non-transitorycomputer readable storage medium has stored therein instructions that,when executed by one or more processors of a device, cause the device toperform or cause performance of any of the methods described herein. Inaccordance with some implementations, a device includes one or moreprocessors, a non-transitory memory, and means for performing or causingperformance of any of the methods described herein.

DESCRIPTION

Numerous details are described in order to provide a thoroughunderstanding of the example implementations shown in the drawings.However, the drawings merely show some example aspects of the presentdisclosure and are therefore not to be considered limiting. Those ofordinary skill in the art will appreciate that other effective aspectsand/or variants do not include all of the specific details describedherein. Moreover, well-known systems, methods, components, devices andcircuits have not been described in exhaustive detail so as not toobscure more pertinent aspects of the example implementations describedherein.

The present disclosure provides methods, systems, and/or devices forgenerating intermediary emergent content. The intermediary emergentcontent spans over an intermediary time duration that is between a firsttime duration that corresponds to a first content item and a second timeduration that corresponds to a second content item. An emergent contentengine synthesizes the intermediary emergent content based on an endstate of the first content item and an initial state of the secondcontent item. The intermediary emergent content allows a user to viewhow a plot progresses between the first content item and the secondcontent item.

A physical setting refers to a world that individuals can sense and/orwith which individuals can interact without assistance of electronicsystems. Physical settings (e.g., a physical forest) include physicalelements (e.g., physical trees, physical structures, and physicalanimals). Individuals can directly interact with and/or sense thephysical setting, such as through touch, sight, smell, hearing, andtaste.

In contrast, a synthesized reality (SR) setting refers to an entirely orpartly computer-created setting that individuals can sense and/or withwhich individuals can interact via an electronic system. In SR, a subsetof an individual's movements is monitored, and, responsive thereto, oneor more attributes of one or more virtual objects in the SR setting ischanged in a manner that conforms with one or more physical laws. Forexample, a SR system may detect an individual walking a few pacesforward and, responsive thereto, adjust graphics and audio presented tothe individual in a manner similar to how such scenery and sounds wouldchange in a physical setting. Modifications to attribute(s) of virtualobject(s) in a SR setting also may be made responsive to representationsof movement (e.g., audio instructions).

An individual may interact with and/or sense a SR object using any oneof his senses, including touch, smell, sight, taste, and sound. Forexample, an individual may interact with and/or sense aural objects thatcreate a multi-dimensional (e.g., three dimensional) or spatial auralsetting, and/or enable aural transparency. Multi-dimensional or spatialaural settings provide an individual with a perception of discrete auralsources in multi-dimensional space. Aural transparency selectivelyincorporates sounds from the physical setting, either with or withoutcomputer-created audio. In some SR settings, an individual may interactwith and/or sense only aural objects.

One example of SR is virtual reality (VR). A VR setting refers to asimulated setting that is designed only to include computer-createdsensory inputs for at least one of the senses. A YR setting includesmultiple virtual objects with which an individual may interact and/orsense. An individual may interact and/or sense virtual objects in the VRsetting through a simulation of a subset of the individual's actionswithin the computer-created setting, and/or through a simulation of theindividual or his presence within the computer-created setting.

Another example of SR is mixed reality (MR). A MR setting refers to asimulated setting that is designed to integrate computer-created sensoryinputs (e.g., virtual objects) with sensory inputs from the physicalsetting, or a representation thereof. On a reality spectrum, a mixedreality setting is between, and does not include, a VR setting at oneend and an entirely physical setting at the other end.

In some MR settings, computer-created sensory inputs may adapt tochanges in sensory inputs from the physical setting. Also, someelectronic systems for presenting MR settings may monitor orientationand/or location with respect to the physical setting to enableinteraction between virtual objects and real objects (which are physicalelements from the physical setting or representations thereof). Forexample, a system may monitor movements so that a virtual plant appearsstationery with respect to a physical building.

One example of mixed reality is augmented reality (AR). An AR settingrefers to a simulated setting in which at least one virtual object issuperimposed over a physical setting, or a representation thereof. Forexample, an electronic system may have an opaque display and at leastone imaging sensor for capturing images or video of the physicalsetting, which are representations of the physical setting. The systemcombines the images or video with virtual objects, and displays thecombination on the opaque display. An individual, using the system,views the physical setting indirectly via the images or video of thephysical setting, and observes the virtual objects superimposed over thephysical setting. When a system uses image sensor(s) to capture imagesof the physical setting, and presents the AR setting on the opaquedisplay using those images, the displayed images are called a videopass-through. Alternatively, an electronic system for displaying an ARsetting may have a transparent or semi-transparent display through whichan individual may view the physical setting directly. The system maydisplay virtual objects on the transparent or semi-transparent display,so that an individual, using the system, observes the virtual objectssuperimposed over the physical setting. In another example, a system maycomprise a projection system that projects virtual objects into thephysical setting. The virtual objects may be projected, for example, ona physical surface or as a holograph, so that an individual, using thesystem, observes the virtual objects superimposed over the physicalsetting.

An augmented reality setting also may refer to a simulated setting inwhich a representation of a physical setting is altered bycomputer-created sensory information. For example, a portion of arepresentation of a physical setting may be graphically altered (e.g.,enlarged), such that the altered portion may still be representative ofbut not a faithfully-reproduced version of the originally capturedimage(s). As another example, in providing video pass-through, a systemmay alter at least one of the sensor images to impose a particularviewpoint different than the viewpoint captured by the image sensor(s).As an additional example, a representation of a physical setting may bealtered by graphically obscuring or excluding portions thereof.

Another example of mixed reality is augmented virtuality (AV). An AVsetting refers to a simulated setting in which a computer-created orvirtual setting incorporates at least one sensory input from thephysical setting. The sensory input(s) from the physical setting may berepresentations of at least one characteristic of the physical setting.For example, a virtual object may assume a color of a physical elementcaptured by imaging sensor(s). In another example, a virtual object mayexhibit characteristics consistent with actual weather conditions in thephysical setting, as identified via imaging, weather-related sensors,and/or online weather data. In yet another example, an augmented realityforest may have virtual trees and structures, but the animals may havefeatures that are accurately reproduced from images taken of physicalanimals.

Many electronic systems enable an individual to interact with and/orsense various SR settings. One example includes head mounted systems. Ahead mounted system may have an opaque display and speaker(s).Alternatively, a head mounted system may be designed to receive anexternal display (e.g., a smartphone). The head mounted system may haveimaging sensor(s) and/or microphones for taking images/video and/orcapturing audio of the physical setting, respectively. A head mountedsystem also may have a transparent or semi-transparent display. Thetransparent or semi-transparent display may incorporate a substratethrough which light representative of images is directed to anindividual's eyes. The display may incorporate LEDs, OLEDs, a digitallight projector, a laser scanning light source, liquid crystal onsilicon, or any combination of these technologies. The substrate throughwhich the light is transmitted may be a light waveguide, opticalcombiner, optical reflector, holographic substrate, or any combinationof these substrates. In one embodiment, the transparent orsemi-transparent display may transition selectively between an opaquestate and a transparent or semi-transparent state. In another example,the electronic system may be a projection-based system. Aprojection-based system may use retinal projection to project imagesonto an individual's retina. Alternatively, a projection system also mayproject virtual objects into a physical setting onto a physical surfaceor as a holograph). Other examples of SR systems include heads updisplays, automotive windshields with the ability to display graphics,windows with the ability to display graphics, lenses with the ability todisplay graphics, headphones or earphones, speaker arrangements, inputmechanisms (e.g., controllers having or not having haptic feedback),tablets, smartphones, and desktop or laptop computers.

FIG. 1 is a block diagram of an example system 100 that synthesizesintermediary emergent content in accordance with some implementations.In various implementations, the system 100 synthesizes intermediaryemergent content that spans a time duration that is between timedurations corresponding to existing content. Briefly, in variousimplementations, the system 100 extracts actions from existing content,analyzes the actions to learn objectives, and utilizes the extractedactions and/or the learned objectives to synthesize the intermediaryemergent content. To that end, in various implementations, the system100 includes an emergent content engine 110, objective-effectuatorengines 120-1, . . . , 120-n, an objective-effectuator engine trainer130, and a plot template datastore 160.

In various implementations, an objective-effectuator represents abehavioral model of an action-performing element. In someimplementations, an objective-effectuator models the behavior of anaction-performing element. In some implementations, anobjective-effectuator performs actions that are within a degree ofsimilarity to actions that the action-performing element performs. Insome implementations, an objective-effectuator models a character fromfictional material such as a movie, a video game, a comic, and/or anovel. In some implementations, an objective-effectuator models anequipment (e.g., machinery such as a plane, a tank, a robot, a car,etc.). In some implementations, an objective-effectuator models atangible object from fictional material or from the real-world (e.g.,from a physical setting). In various implementations, anobjective-effectuator is referred to as an SR agent, theobjective-effectuator engines 120-1, . . . , 120-n are referred to as SRagent engines, and the objective-effectuator engine trainer 130 isreferred to as an SR agent engine trainer.

In various implementations, an objective-effectuator effectuates anaction in order to advance (e.g., complete or satisfy) an objective. Insome implementations, an objective-effectuator is associated with aparticular objective, and the objective-effectuator effectuates actionsthat improve the likelihood of advancing that particular objective.Referring to FIG. 1, the objective-effectuator engines 120-1, . . . ,120-n generate actions 122-1, . . . , 122-n for correspondingobjective-effectuators. In some implementations, the emergent contentengine 110 provides objectives 112 to the objective-effectuator engines120-1, . . . , 120-n. The objective-effectuator engines 120-1, . . . ,120-n utilize the objectives 112 to generate the actions 122-1, . . . ,122-n.

In various implementations, the objective-effectuator engine trainer 130(“trainer 130”, hereinafter for the sake of brevity) trains theobjective-effectuator engines 120-1, . . . , 120-n. In the example ofFIG. 1, the trainer 130 trains the objective-effectuator engines 120-1,. . . , 120-n based on a first content item 140 and a second contentitem 150. As illustrated in FIG. 1, the first content item 140 spans afirst time duration T1, and the second content item 150 spans a secondtime duration T3. FIG. 1 illustrates an intermediary duration T2 betweenthe first time duration T1 and the second time duration T3. There is noexisting content that spans the intermediary duration T2. After thetrainer 130 utilizes the first content item 140 and the second contentitem 150 to train the objective-effectuator engines 120-1, . . . ,120-n, the objective-effectuator engines 120-1, . . . , 120-n generatean intermediary emergent content item that spans the intermediaryduration T2.

In some implementations, the trainer 130 obtains actions that areextracted from existing content. In the example of FIG. 1, the trainer130 obtains a first set of actions 142 that are extracted from the firstcontent item 140, and a second set of actions 152 that are extractedfrom the second content item 150. In some implementations, the first setof actions 142 includes actions that action-performing elements (e.g.,characters, equipment, etc.) perform in the first content item 140 toadvance a plot/storyline of the first content item 140. In someimplementations, the second set of actions 152 includes actions thataction-performing elements perform in the second content item 150 toadvance a plot/storyline of the second content item 150. In someimplementations, a set of actions are extracted from a content item byperforming scene analysis on the content item in order to identify theaction-performing elements and the actions that the action-performingelements perform. In some implementations, the trainer 130 determinesparameters 132 for the objective-effectuator engines 120-1, . . . ,120-n based on the extracted actions. In some implementations, theparameters 132 include neural network parameters.

In some implementations, the objective-effectuator engines 120-1, . . ., 120-n obtain objectives that are associated with existing content. Insome implementations, the objectives associated with existing contentare determined based on (e.g., derived from) the actions that areextracted from the existing content. In the example of FIG. 1, theobjective-effectuator engines 120-1, . . . , 120-n obtain a first set oflearned objectives 144 that are determined based on the first set ofactions 142. The objective-effectuator engines 120-1, . . . , 120-nobtain a second set of learned objectives 154 that are determined basedon the second set of actions 152. In some implementations, learnedobjectives are determined by performing semantic analysis on theextracted actions. In the example of FIG. 1, the objective-effectuatorengines 120-1, . . . , 120-n obtain the first set of learned objectives144 and the second set of learned objectives 154 via an aggregator 170.In some implementations, the aggregator 170 aggregates (e.g., packages)the first set of learned objectives 144 and the second set of learnedobjectives 154. In some implementations, the aggregator 170 provides thesets of learned objectives 144 and 154 to a selector 172 that forwardsthe sets of learned objectives 144 and 154 to the objective-effectuatorengines 120-1, . . . , 120-n during a training phase.

During the training phase of the objective-effectuator engines 120-1, .. . , 120-n, the objective-effectuator engines 120-1, . . . , 120-ngenerate the actions 122-1, . . . , 122-n based on learned objectives(e.g., the sets of learned objectives 144 and 154). The trainer 130compares the generated actions 122-1, . . . , 122-n with the extractedactions. If the generated actions 122-1, . . . , 122-n are within adegree of similarity to the extracted actions, then the trainer 130determines that the training of the objective-effectuator engines 120-1,. . . , 120-n is complete. If the generated actions 122-1, . . . , 122-nare not within a degree of similarity to the extracted actions, then thetrainer 130 adjusts the parameters 132 based on a difference between thegenerated actions 122-1, . . . , 122-n and the extracted actions.

During the training phase of the objective-effectuator engines 120-1, .. . , 120-n, the objective-effectuator engines 120-1, . . . , 120-ngenerate actions 122-1, . . . , 122-n for the first time duration T1.The trainer 130 compares the actions 122-1, . . . , 122-n generated forthe first time duration T1 with the first set of extracted actions 142(“extracted actions 142”, hereinafter for the sake of brevity). In someimplementations, if the actions 122-1, . . . , 122-n generated for thefirst time duration T1 match (e.g., are within a degree of similarityto) the extracted actions 142, then the trainer 130 determines that thetraining of the objective-effectuator engines 120-1, . . . , 120-n iscomplete. In some implementations, if the actions 122-1, . . . , 122-ngenerated for the first time duration T1 match the extracted actions142, then the trainer 130 determines whether the objective-effectuatorengines 120-1, . . . , 120-n are able to generate actions 122-1, . . . ,122-n for the second time duration T3 that match the second set ofextracted actions 152 (“the extracted actions 152”, hereinafter for thesake of brevity). In some implementations, the trainer 130 continuesadjusting the parameters 132 until the actions 122-1, . . . , 122-ngenerated for the first time duration T1 match the extracted actions 142from the first content item 140.

During the training phase of the objective-effectuator engines 120-1, .. . , 120-n, the objective-effectuator engines 120-1, . . . , 120-ngenerate actions 122-1, . . . , 122-n for the second time duration T3.The trainer 130 compares the actions 122-1, . . . , 122-n generated forthe second time duration T3 with the extracted actions 152. In someimplementations, if the actions 122-1, . . . , 122-n generated for thesecond time duration T3 match (e.g., are within a degree of similarityto) the extraction actions 152, then the trainer 130 determines that thetraining of the objective-effectuator engines 120-1, . . . , 120-n iscomplete. In some implementations, the trainer 130 continues adjustingthe parameters 132 until the actions 122-1, . . . , 122-n generated forthe second time duration T3 match the extracted actions 152 from thesecond content item 150.

In some implementations, after determining that the training of theobjective-effectuator engines 120-1, . . . , 120-n is complete, thetrainer 130 instructs the selector 172 to stop forwarding the learnedobjectives 144 and 154 to the objective-effectuator engines 120-1, . . ., 120-n.

During the training phase, the objective-effectuator engines 120-1, . .. , 120-n provide the generated actions 122-1, . . . , 122-n to theemergent content engine 110, so that the emergent content engine 110 canutilize the actions 122-1, . . . , 122-n as training data. During thetraining phase, the objective-effectuator engines 120-1, . . . , 120-nprovide the generated actions 122-1, . . . , 122-n to themselves, sothat the parameters 132 of the objective-effectuator engines 120-1, . .. , 120-n can be adjusted based on the generated actions 122-1, . . . ,122-n.

In the production phase, the objective-effectuator engines 120-1, . . ., 120-n generate actions 122-1, . . . , 122-n that collectively form anintermediary emergent content item that spans the intermediary durationT2. In some implementations, the emergent content engine 110 generatesobjectives 112 (e.g., a set of bounded objectives) based on an end stateof the first content item 140 and an initial state of the second contentitem 150. The emergent content engine 110 provides the objectives 112 tothe objective-effectuator engines 120-1, . . . , 120-n via the selector172. In the production phase, the selector 172 forwards the objectives112 to the objective-effectuator engines 120-1, . . . , 120-n instead offorwarding the learned objectives 144 and 154.

The objective-effectuator engines 120-1, . . . , 120-n utilize theobjectives 112 provided by the emergent content engine 110 to generateactions 122-1, . . . , 122-n for the intermediary duration T2. Theactions 122-1, . . . , 122-n for the intermediary duration T2collectively form the intermediary emergent content item that spans theintermediary duration T2. In some implementations, theobjective-effectuator engines 120-1, . . . , 120-n provide the actions122-1, . . . , 122-n for the intermediary duration T2 to a rendering anddisplay pipeline, so that the intermediary emergent content item can bepresented to a user.

In some implementations, the plot template datastore 160 stores variousplot templates 162. In some implementations, each plot template 162corresponds to a type of plot (e.g., a type of storyline). In someimplementations, the plot templates 162 include a plot template for amystery plot. In some implementations, the plot templates 162 include aplot template for a disaster plot. In some implementations, the plottemplates 162 include a plot template for a comedy plot. In someimplementations, the emergent content engine 110 selects a plot template162 from the plot template datastore 160. In some implementations, theobjectives 112 are a function of the plot template 162 that the emergentcontent engine 110 selects from the plot template datastore 160. In someimplementations, the objectives 112 advance a plot corresponding withthe plot template 162 that the emergent content engine 110 selects.

In some implementations, the emergent content engine 110 selects one ofthe plot templates 162 from the plot template datastore 160 based on theend state of the first content item 140 and/or the initial state of thesecond content item 150. In some implementations, the emergent contentengine 110 selects one of the plot templates 162 based on the learnedobjectives 144 and/or 154. In some implementations, the learnedobjectives 144 and/or 154 indicate a pattern that matches one of theplot templates 162. In such implementations, the emergent content engine110 selects the plot template 162 that most closely matches the learnedobjectives 144 and/or 154. In some implementations, the emergent contentengine 110 selects one of the plot templates 162 based on a user input.For example, in some implementations, the user input specifies which ofthe plot templates 162 is to be used for the intermediary emergentcontent item.

FIG. 2A is a diagram that illustrates an end state 146 of the firstcontent item 140 and an initial state 156 of the second content item150. In various implementations, the first content item 140 has variousstates that correspond to different times t1,0, . . . t1,n within thefirst time duration T1. The end state 146 of the first content item 140corresponds to time t1,n. In various implementations, the second contentitem 150 has various states that correspond to different times t3,0, . .. t3,n within the second time duration T3. The initial state 156 of thesecond content item 150 corresponds to time t3,n.

In some implementations, the end state 146 of the first content item 140indicates how the first content item 140 ends. In some implementations,the end state 146 of the first content item 140 indicates variousaction-performing elements that are present at time t1,n. In someimplementations, the end state 146 of the first content item 140indicates locations of the action-performing elements, actions that theaction-performing elements are performing at time t1,n, a geographicallocation where the last scene of the first content item 140 takes place,and/or environmental conditions within the last scene of the firstcontent item 140. In the example of FIG. 2A, the end state 146 of thefirst content item 140 includes a boy action-performing element 202, agirl action-performing element 204, a robot action-performing element206, and a drone action-performing element 208.

In some implementations, the initial state 156 of the second contentitem 150 indicates how the second content item 150 starts. In someimplementations, the initial state 156 of the second content item 150indicates various action-performing elements that are present at timet3,0. In some implementations, the initial state 156 of the secondcontent item 150 indicates locations of the action-performing elements,actions that the action-performing elements are performing at time t3,0,a geographical location where the first scene of the second content item150 takes place, and/or environmental conditions within the first sceneof the second content item 150. In the example of FIG. 2A, the initialstate 156 of the second content item 150 includes the boyaction-performing element 202 and the robot action-performing element206.

FIG. 2B illustrates a plot template 162 a that is selected from the plottemplates 162 shown in FIG. 1. In the example of FIG. 2B, the plottemplate 162 a includes a first interim objective 210 at time t2,1, asecond interim objective 212 at time t2,7, a third interim objective 214at time t2,10, a fourth interim objective 216 at time t2,15, and a fifthinterim objective 218 at time t2,16. The plot template 162 a indicates arelationship between the various interim objectives 210-218. Therelationship between the interim objectives 210-218 indicates the plotfor the intermediary emergent content item that spans the intermediaryduration T2. For example, the relationship between the interimobjectives 210-218 indicates whether the plot is a mystery plot, adisaster plot, a suspense plot, a comedy plot, etc.

FIG. 2C illustrates an example intermediary emergent content item 220 atits initial state 220 a (at time t2,0). The intermediary emergentcontent item 220 includes an SR representation of a boyobjective-effectuator 222 (“boy objective-effectuator 222”, hereinafterfor the sake of brevity), an SR representation of a girlobjective-effectuator 224 (“girl objective-effectuator 224”, hereinafterfor the sake of brevity), an SR representation of a robotobjective-effectuator 226 (“robot objective-effectuator 226”,hereinafter for the sake of brevity), and an SR representation of adrone objective-effectuator 228 (“drone objective-effectuator 228”,hereinafter for the sake of brevity). In the example of FIG. 2C, the boyobjective-effectuator 222 models the behavior of the boyaction-performing element 202. The girl objective-effectuator 224 modelsthe behavior of the girl action-performing element 204. The robotobjective-effectuator 226 models the behavior of the robotaction-performing element 206. The drone objective-effectuator 228models the behavior of the drone action-performing element 208.

As illustrated in FIG. 2C, in some implementations, the initial state220 a of the intermediary emergent content item 220 matches (e.g., isidentical to) the end state 146 of the first content item 140. As such,in some implementations, the state of the intermediary emergent contentitem 220 at time t2,0 is a replica of the end state 146 of the firstcontent item 140. To that end, the position and actions of the boyobjective-effectuator 222 at time t2,0 are the same as the position andactions of the boy action-performing 202 at time t1,n. The position andactions of the girl objective-effectuator 224 at time t2,0 are the sameas the position and actions of the girl action-performing element 204 attime t1,n. The position and actions of the robot objective-effectuator226 at time t2,0 are the same as the position and actions of the robotaction-performing element 206 at time t1,n. The position and actions ofthe drone objective-effectuator 228 at time t2,0 are the same as theposition and actions of the drone action-performing element 208 at timet1,n.

FIGS. 2D and 2E illustrate intermediate states 220 b and 220 c,respectively, of the intermediary emergent content item 220. Asillustrated in FIG. 2D, the intermediate state 220 b of the intermediaryemergent content item 220 corresponds to time t2,7. In the intermediatestate 220 b, the boy objective-effectuator 222 and the girlobjective-effectuator 224 are performing actions that are different fromthe actions that the boy objective-effectuator 222 and the girlobjective-effectuator 224 performed at the initial state 220 a of theintermediary emergent content item 220. For example, as illustrated inFIG. 2D, the girl objective-effectuator 224 has turned around and theboy objective-effectuator 222 has raised its arm.

As illustrated in FIG. 2E, the intermediate state 220 c of theintermediary emergent content item 220 corresponds to time t2,15. In theintermediate state 220 c, the boy objective-effectuator 222 and the girlobjective-effectuator 224 are performing actions that are different fromthe actions that the boy objective-effectuator 222 and the girlobjective-effectuator 224 performed at the initial state 220 a and theintermediate state 220 b of the intermediary emergent content item 220.For example, as illustrated in FIG. 2E, the girl objective-effectuator224 and the drone objective-effectuator 228 are about to exit from thescene, and the robot objective-effectuator 226 is moving towards the boyobjective-effectuator 222.

FIG. 2F illustrates an end state 220 d of the intermediary emergentcontent item 220 at time t2,n. As illustrated in FIG. 2F, in someimplementations, the end state 220 d of the intermediary emergentcontent item 220 matches (e.g., is identical to) the initial state 156of the second content item 150. As such, in some implementations, thestate of the intermediary emergent content item 220 at time t2,n is areplica of the initial state 156 of the second content item 150. To thatend, the position and actions of the boy objective-effectuator 222 attime t2,n are the same as the position and actions of the boyaction-performing element 202 at time t3,0. The position and actions ofthe robot objective-effectuator 226 at time t2,n are the same as theposition and actions of the robot action-performing element 206 at timet3,n.

FIG. 3A illustrates an example system 100 a for training anobjective-effectuator engine 120. In some implementations, the system100 a includes the objective-effectuator engine 120, the trainer 130, anaction extractor 174 and an objective determiner 176.

In some implementations, the action extractor 174 obtains the firstcontent item 140, and extracts actions 142 from the first content item140. In some implementations, the action extractor 174 performs sceneanalysis to identify the extracted actions 142 that are being performedin the first content item 140. Although FIG. 3A shows a single contentitem, in some implementations, the action extractor 174 obtains multiplecontent items (e.g., a series of content items, for example, an entireseason with numerous episodes). In some implementations, the actionextractor 174 provides the extracted actions 142 to the trainer 130and/or the objective determiner 176.

In some implementations, the objective determiner 176 determines thefirst set of objectives 144 based on the extracted actions 142. In someimplementations, the objective determiner 176 derives the first set ofobjectives 144 from the extracted actions 142. In some implementations,the objective determiner 176 learns the first set of objectives 144 byanalyzing the extracted actions 142. As such, in some implementations,the first set of objectives 144 are referred to as learned objectives orderived objectives. In some implementations, the objective determiner176 includes a semantic analyzer that performs semantic analysis on theextracted actions 142 to determine the first set of objectives 144. Forexample, in some implementations, the objective determiner 176 performssemantic analysis on text that corresponds to dialogs that are spoken bythe action-performing elements in the first content item 140.

In some implementations, the objective determiner 176 provides the firstset of objectives 144 to the objective-effectuator engine 120. In someimplementations, the objective-effectuator engine 120 generates actions122 based on the first set of objectives 144. For example, in someimplementations, the objective-effectuator engine 120 generates actions122 that advance (e.g., complete or satisfy) the first set of objectives144. In some implementations, at least during the training phase, theobjective-effectuator engine 120 provides the generated actions 122 tothe trainer 130.

In some implementations, the trainer 130 includes an action comparator134 and an objective-effectuator engine parameter determiner 136(“parameter determiner 136”, hereinafter for the sake of brevity). Insome implementations, the parameter determiner 136 determines theparameters 132 based on the extracted actions 142. In someimplementations, the action comparator 134 compares the generatedactions 122 with the extracted actions 142. If the action comparator 134determines that the generated actions 122 match the extracted actions142, then the trainer 130 determines that the training of theobjective-effectuator engine 120 is complete. If the action comparator134 determines that the generated actions 122 do not match the extractedactions 142, then the parameter determiner 136 adjusts the parameters132. In some implementations, the parameter determiner 136 adjusts theparameters 132 based on a difference between the generated actions 122and the extracted actions 142. In some implementations, the adjustmentto the parameters 132 is a function of (e.g., directly proportional to)the difference between the generated actions 122 and the extractedactions 142.

FIG. 3B illustrates an example system 100 b for synthesizingintermediary emergent content items. In some implementations, the system100 b includes a state obtainer 178 and an intermediary emergent contentsynthesizer 300. In the example of FIG. 3B, the intermediary emergentcontent synthesizer 300 includes the emergent content engine 110, theobjective-effectuator engines 120-1, . . . , 120-n, and the plottemplate datastore 160.

In some implementations, the state obtainer 178 obtains the end state146 of the first content item 140 and the initial state 156 of thesecond content item 150. In some implementations, the state obtainer 178obtains the first content item 140. In such implementations, the stateobtainer 178 analyzes the first content item 140 to determine the endstate 146 of the first content item 140. For example, in someimplementations, the state obtainer 178 performs scene analysis on thefirst content item 140 to identify the action-performing elements thatare in the first content item 140, and the locations and actions of theaction-performing elements at the end of the first content item. In someimplementations, the state obtainer 178 provides the end state 146 ofthe first content item 140 to the intermediary emergent contentsynthesizer 300.

In some implementations, the state obtainer 178 obtains the secondcontent item 150. In such implementations, the state obtainer 178analyzes the second content item 150 to determine the initial state 156of the second content item 150. For example, in some implementations,the state obtainer 178 performs scene analysis on the second contentitem 150 to identify the action-performing elements that are in thesecond content item 150, and the locations and actions of theaction-performing elements at the beginning of the second content item.In some implementations, the state obtainer 178 provides the initialstate 156 of the second content item 150 to the intermediary emergentcontent synthesizer 300.

In some implementations, the intermediary emergent content synthesizer300 utilizes the end state 146 of the first content item 140 and theinitial state 156 of the second content item 150 to synthesize anintermediary emergent content item 310. The intermediary emergentcontent item 310 spans the intermediary duration T2 that is between thefirst time duration T1 corresponding to the first content item 140 andthe second time duration T2 corresponding to the second content item150.

In some implementations, the emergent content engine 110 determines aset of bounded objectives (e.g., the objectives 112) based on the endstate 146 of the first content item 140 and the initial state 156 of thesecond content item 150. In some implementations, the emergent contentengine 110 selects a plot template 162 from the plot template datastore160. In such implementations, the objectives 112 are a function of theselected plot template 162. In some implementations, the objectives 112are bounded by a first set of objectives (e.g., the first set ofobjectives 144 shown in FIG. 1) associated with the end state 146 of thefirst content item 140 and a second set of objectives (e.g., the secondset of objectives 154 shown in FIG. 1) associated with the initial state156 of the second content item 150.

In some implementations, the objective-effectuator engines 120-1, . . ., 120-n obtain the objectives 112 from the emergent content engine 1120.The objective-effectuator engines 120-1, . . . , 120-n generaterespective actions 122-1, . . . , 122-n based on the objectives 112. Insome implementations, the actions 122-1, . . . , 122-n collectively formthe intermediary emergent content item 310.

FIG. 4A is a block diagram of a system 400 in accordance with someimplementations. In some implementations, the system 400 includes aneural network system 410 (“neural network 410”, hereinafter for thesake of brevity) and the trainer 130. In some implementations, theneural network 410 implements an objective-effectuator engine (e.g., theobjective-effectuator engine 120 shown in FIG. 3A). In someimplementations, the trainer 130 provides neural network parameters 432(e.g., neural network weights, for example, the parameters 132 shown inFIG. 3A) to the neural network 410.

In some implementations, the neural network 410 includes a longshort-term memory (LSTM) recurrent neural network (RNN). In the exampleof FIG. 4A, the neural network 410 includes an input layer 420, a firsthidden layer 422, a second hidden layer 424, a classification layer 426,and an action selection module 428. While the neural network 410includes two hidden layers as an example, those of ordinary skill in theart will appreciate from the present disclosure that one or moreadditional hidden layers are also present in various implementations.Adding additional hidden layers adds to the computational complexity andmemory demands, but may improve performance for some applications.

In various implementations, the input layer 420 receives various inputs.In some implementations, the input layer 420 obtains (e.g., receives) aset of objectives that are derived from a set of extracted actions. Inthe example of FIG. 4A, the input layer 420 receives the first set oflearned objectives 144 that were derived from the extracted actions 142of the first content item 140 shown in FIG. 3A. In some implementations,the neural network 410 includes a feature extraction module (not shown)that generates a feature stream (e.g., a feature vector) based on thefirst set of learned objectives 144. In such implementations, thefeature extraction module provides the feature stream to the input layer420. As such, in some implementations, the input layer 420 receives afeature stream that is a function of the learned objectives 144. Invarious implementations, the input layer 420 includes a number of LSTMlogic units 420 a, which are also referred to as neurons or models ofneurons by those of ordinary skill in the art. In some suchimplementations, an input matrix from the features to the LSTM logicunits 420 a includes rectangular matrices. The size of this matrix is afunction of the number of features included in the feature stream.

In some implementations, the first hidden layer 422 includes a number ofLSTM logic units 422 a. In some implementations, the number of LSTMlogic units 422 a ranges between approximately 10-500. Those of ordinaryskill in the art will appreciate that, in such implementations, thenumber of LSTM logic units per layer is orders of magnitude smaller thanpreviously known approaches (being of the order of O(10¹)-O(10²)), whichallows such implementations to be embedded in highlyresource-constrained devices. As illustrated in the example of FIG. 4A,the first hidden layer 422 receives its inputs from the input layer 420.

In some implementations, the second hidden layer 424 includes a numberof LSTM logic units 424 a. In some implementations, the number of LSTMlogic units 424 a is the same as or similar to the number of LSTM logicunits 420 a in the input layer 420 or the number of LSTM logic units 422a in the first hidden layer 422. As illustrated in the example of FIG.4A, the second hidden layer 424 receives its inputs from the firsthidden layer 422. Additionally or alternatively, in someimplementations, the second hidden layer 424 receives its inputs fromthe input layer 420.

In some implementations, the classification layer 426 includes a numberof LSTM logic units 426 a. In some implementations, the number of LSTMlogic units 426 a is the same as or similar to the number of LSTM logicunits 420 a in the input layer 420, the number of LSTM logic units 422 ain the first hidden layer 422 or the number of LSTM logic units 424 a inthe second hidden layer 424. In some implementations, the classificationlayer 426 includes an implementation of a multinomial logistic function(e.g., a soft-max function) that produces a number of outputs that isapproximately equal to a number of possible actions. In someimplementations, each output includes a probability or a confidencemeasure of the corresponding action matching the extracted actions 142.

In some implementations, the action selection module 428 generates theactions 122 by selecting the top N action candidates provided by theclassification layer 426. In some implementations, the top N actioncandidates are likely to match the extracted actions 142. In someimplementations, the action selection module 428 provides the generatedactions 122 to the trainer 130, so that the trainer 130 can compare thegenerated actions 122 with the extracted actions 142.

In some implementations, the trainer 130 (e.g., the action comparator134) compares the generated actions 122 with the extracted actions 142.If the generated actions 122 match the extracted actions 142, then thetrainer 130 determines that the neural network 410 has been trained. Ifthe generated actions 122 do not match the extracted actions 142, thenthe trainer 130 (e.g., the parameter determiner 136) adjusts the neuralnetwork parameters 432. In some implementations, the trainer 130 (e.g.,the parameter determiner 136) iteratively adjusts the neural networkparameters 432 until the generated actions 122 match the extractedactions 142. In some implementations, the generated actions 122 matchthe extracted actions 142 if the generated actions 122 are within adegree of similarity to the extracted actions 142.

In some implementations, the neural network 410 is trained using asingle content item (e.g., the first content item 140 shown in FIG. 3A).In some implementations, the neural network 410 is trained usingmultiple content items (e.g., the first content item 140 and the secondcontent item 150 shown in FIG. 3B). In some implementations, the neuralnetwork 410 is trained using a series of content items (e.g., an entireseason of a show with numerous episodes). In some implementations, theneural network 410 is trained using multiple series of content items(e.g., multiple seasons of a show, where each season has numerousepisodes).

Referring to FIG. 4B, the neural network 410 generates the intermediaryemergent content item 310 based on one or more inputs. In someimplementations, the neural network 410 generates the intermediaryemergent content item 310 based on a set of bounded objectives (e.g.,based on the set of bounded objectives 112 shown in FIG. 3B). Asdiscussed herein, in some implementations, the bounded objectives arederived from an end state of a first content item and an initial stateof a second content item. For example, as shown in FIG. 3B, theobjectives 112 are derived from the end state 146 of the first contentitem 140 and the initial state 156 of the second content item 150.

In some implementations, the neural network 410 utilizes a plot template162 to generate the intermediary emergent content item 310. In someimplementations, the actions 122 generated by the neural network 410 area function of the plot template 162 a. For example, if the plot template162 a is a comedy plot template, then the actions 122 generated by theneural network 410 satisfy the comedy plot template.

In some implementations, the neural network 410 utilizes sceneinformation 440 (e.g., environmental information regarding an SRsetting) to generate the intermediary emergent content item 310. In someimplementations, the scene information 440 indicates a boundary for thescene (e.g., a boundary for the SR setting). In such implementations,the actions 122 that form the intermediary emergent content item 310 areperformed within the boundary of the scene. In some implementations, thescene information 440 indicates environmental information regarding thescene. In such implementations, the actions 122 that form theintermediary emergent content item 310 are generated based on theenvironment of the scene.

In some implementations, the neural network 410 utilizes informationregarding instantiated equipment/characters 442 to generate theintermediary emergent content item 310. For example, in someimplementations, the actions 122 that form the intermediary emergentcontent item 310 include interacting with the instantiatedequipment/characters 442.

In some implementations, the neural network 410 utilizes user-specifiedconstraints 444 to generate the intermediary emergent content item 310.In some implementations, the actions 122 that form the intermediaryemergent content item 310 satisfy the user-specified constraints 444.For example, in some implementations, the user-specified constraints 444specify a location where the intermediary emergent content item 310 isto take place. In such implementations, the actions 122 that form theintermediary emergent content item 310 take place at the locationspecified in the user-specified constraints 444. In someimplementations, the user-specified constraints 444 specify specificequipment/characters that are to be included in the intermediaryemergent content item 310. In such implementations, the actions 122 thatform the intermediary emergent content item 310 are associated with theequipment/characters indicated in the user-specified constraints 444.

FIG. 5A is a diagram of an example user interface 500 in accordance withsome implementations. The user interface 500 displays informationregarding a content series (e.g., a show). In the example of FIG. 5A,the user interface 500 includes a show name 502, a season number 504, arating 506, a first episode representation 510, a second episoderepresentation 512, a third episode representation 514, a fourth episoderepresentation 516, and play affordances 520 for each episode.

FIG. 5B illustrates a user input 530 a that corresponds to a request tocreate an intermediary emergent content item (e.g., gap content, forexample, a gap episode). In the example of FIG. 5B, detecting the userinput 530 a includes detecting contacts that are moving the firstepisode representation 510 and the second episode representation 512away from each other. In some implementations, the user input 530 aincludes a zoom gesture that zooms between the first episoderepresentation 510 and the second episode representation 512. In someimplementations, the user input 530 a corresponds to a request to createan intermediary emergent content item that spans an intermediary timeduration between the first episode and the second episode of the show.

FIG. 5C illustrates a prompt 540 that includes a standard generationaffordance 542 and a customized generation affordance 544. In someimplementations, the user interface 500 displays the prompt 540 inresponse to receiving the user input 530 a. In some implementations, auser selection of the standard generation affordance 542 corresponds toa request to generate the intermediary emergent content item withdefault settings. In some implementations, a user selection of thecustomized generation affordance 544 corresponds to a request togenerate the intermediary emergent content item with customizedsettings.

FIG. 5D illustrates a user input 530 b that selects the standardgeneration affordance 542. In some implementations, the user input 530 bcorresponds to a request to generate the intermediary emergent contentitem with default settings.

FIG. 5E illustrates a gap content representation 511 that represents anintermediary emergent content item (e.g., gap content) that spans anintermediary time duration between the first episode and the secondepisode. As shown in FIG. 5E, the gap content representation 511 isassociated with a play affordance 520. A user selection of the playaffordance 520 triggers playback of the gap content.

FIG. 5F illustrates a user input 530 c that selects the customizedgeneration affordance 544. In some implementations, the user input 530 ccorresponds to a request to generate the gap content based on customizedsettings (e.g., instead of or in addition to default settings).

FIG. 5G illustrates an example customization screen 550 that allows auser to customize the generation of the gap content. In the example ofFIG. 5G, the customization screen 550 includes plot affordances 552,location affordances 554, action-performing element affordances 556,time affordances 558, and a generation affordance 560.

The plot affordances 552 allow a user to select a plot template for thegap content. For example, in some implementations, the plot affordances552 allow the user to select one of the plot templates 162 shown in FIG.1.

The location affordances 554 allow a user to select a location for thegap content. In some implementations, the location affordances 554 allowthe user to select the location where the first episode ended. In someimplementations, the location affordances 554 allow the user to selectthe location where the second episode begins. In some implementations,the location affordances 554 allow the user to specify a location thatis different from the locations of the first and second episodes.

The action-performing element affordances 556 allow a user to selectaction-performing elements for the gap content. In some implementations,the action-performing element affordances 556 allow the user to selectaction-performing elements from the first episode. In someimplementations, the action-performing element affordances 556 allow theuser to select action-performing elements from the second episode. Insome implementations, the action-performing element affordances 556allow the user to select other action-performing elements that were notincluded present in the first and second episodes.

The time affordances 558 allow a user to select a time duration for thegap content. In some implementations, the time affordances allow theuser to specify a time duration for the gap content that is differentfrom suggested time durations.

The generation affordance 560 allows a user to generate the gap contentbased on the selections of the plot affordances 552, the locationaffordances 554, the action-performing element affordances 556 and thetime affordances 558.

Referring to FIG. 5H, in some implementations, some of the plotaffordances 552 are not selectable. In the example of FIG. 5H, therescue plot affordance is not selectable. In some implementations,certain plot affordances are not selectable based on the type of plotsthat the first and second episodes are associated with. For example, ifthe first and second episodes are associated with a comedy plot, thenthe rescue plot affordance is not selectable for the gap content.

In some implementations, some of the location affordances 554 are notselectable. In the example of FIG. 5H, the location where the firstepisode ended is not available for the gap content (e.g., because thelocation was damaged/destroyed at the end of the first episode).

In some implementations, some of the action-performing elementaffordances are not selectable. For example, if a particularaction-performing element died during the first episode, then thecorresponding action-performing element affordance is not selectablebecause that particular action-performing element is no longer availablefor the gap content.

Referring to FIG. 5I, in some implementations, the gap contentrepresentation 511 is associated with a modification affordance 570 anda sharing affordance 580. The modification affordance 570 allows a userto modify the gap content. The sharing affordance 580 allows a user toshare the gap content. FIG. 5I illustrates a user input 530 d selectingthe modification affordance 570.

FIG. 5J illustrates a modification screen 572 that is displayed inresponse to the user input 530 d selecting the modification affordance570. In some implementations, the modification screen 572 includes theplot affordances 552, the location affordances 554, theaction-performing element affordances 556, and the time affordances 558.As such, the modification screen 572 allows the user to change the plottemplate, the location, the action-performing elements, and/or the timeduration for the gap content.

FIG. 5K illustrates a user input 530 e selecting the sharing affordance580. In some implementations, the user input 530 e corresponds to arequest to share the gap content with another user.

FIG. 5L illustrates a share sheet 590 that is displayed in response tothe user input 530 e. In some implementations, the share sheet 590includes a local sharing affordance 592, a messaging affordance 594, amail affordance 596 and a publish affordance 598. In someimplementations, the local sharing affordance 592 allows the user toshare the gap content with nearby devices. The messaging affordance 594allows the user to send the gap content via a message (e.g., an instantmessage). The mail affordance 596 allows the user to send the gapcontent via e-mail. The publish affordance 598 allows the user topublish the gap content (e.g., on a content store) and obtain a creditfor publishing the gap content.

FIG. 6A is a flowchart representation of a method 600 of generating anintermediary emergent content item. In various implementations, themethod 600 is performed by a device with a non-transitory memory and oneor more processors coupled with the non-transitory memory (e.g., thedevice 900 shown in FIG. 9). In some implementations, the method 600 isperformed by processing logic, including hardware, firmware, software,or a combination thereof. In some implementations, the method 600 isperformed by a processor executing code stored in a non-transitorycomputer-readable medium (e.g., a memory). Briefly, in someimplementations, the method 600 includes obtaining an end state of afirst content item, obtaining an initial state of a second content item,and synthesizing an intermediary emergent content item based on the endstate of the first content item and the initial state of the secondcontent item.

As represented by block 610, in some implementations, the method 600includes obtaining an end state of a first content item spanning a firsttime duration. For example, as illustrated in FIG. 3B, the method 600includes obtaining the end state 146 of the first content item 140spanning the first time duration T1. In some implementations, the endstate of the first content item indicates a first state of anobjective-effectuator at the end of the first time duration. Forexample, as illustrated in FIG. 2B, the end state 146 of the firstcontent item 140 indicates a first state of the boyobjective-effectuator 222.

As represented by block 620, in some implementations, the method 600includes obtaining an initial state of a second content item spanning asecond time duration. For example, as illustrated in FIG. 3B, the method600 includes obtaining the initial state 156 of the second content item150 spanning the second time duration T2. In some implementations, theinitial state of the second content item indicates a second state of theobjective-effectuator at the beginning of the second time duration. Forexample, as illustrated in FIG. 2F, the initial state 156 of the secondcontent item 150 indicates a second state of the boyobjective-effectuator 222.

As represented by block 630, in some implementations, the method 600includes synthesizing an intermediary emergent content item spanningover an intermediary time duration that is between the end of the firsttime duration and the beginning of the second time duration. Forexample, as illustrated in FIG. 3B, the intermediary emergent contentitem 310 spans the intermediary duration T2 that is between the firsttime duration T1 and the second time duration T3.

As represented by block 632, in some implementations, the method 600includes generating a set of bounded objectives for theobjective-effectuator by providing the end state of the first contentitem and the initial state of the second content item to an emergentcontent engine. For example, generating the set of bounded objectives112 shown in FIG. 3B by providing the end state 146 of the first contentitem 140 and the initial state 156 of the second content item 150 to theemergent content engine 110. In some implementations, the set of boundedobjectives are bounded by the end state of the first content item andthe initial state of the second content item. For example, theobjectives 112 shown in FIG. 3B are bounded by the end state 146 of thefirst content item 140 and the initial state 156 of the second contentitem 150.

As represented by block 634, in some implementations, the method 600includes generating a set of actions for the objective-effectuator byproviding the set of bounded objectives to an objective-effectuatorengine. For example, generating the set of actions 122-1 shown in FIG.3B for an objective-effectuator (e.g., the boy objective-effectuator 222shown in FIG. 2C) by providing the set of bounded objectives 112 to theobjective-effectuator engine 120-1.

As represented by block 636, in some implementations, the method 600includes rendering the intermediary emergent content item for display.For example, as shown in FIG. 1, the generated actions 122-1, . . . ,122-n that form the intermediary emergent content item are sent to arendering and display pipeline.

In various implementations, synthesizing the intermediary emergentcontent item allows the user to view new content that was not originallycreated by the entity that created the first content item and the secondcontent item. As such, synthesizing the intermediary emergent contentitem provides the user with an option to watch additional contentthereby enhancing user experience and increasing the operability of thedevice.

Referring to FIG. 6B, as represented by block 630 a, in someimplementations, the initial state of the intermediary emergent contentitem is within a degree of similarity to the end state of the firstcontent item. For example, as illustrated in FIG. 2C, the initial state220 a of the intermediary emergent content item 220 matches (e.g., isidentical to) the end state 146 of the first content item 140. Invarious implementations, the initial state of the intermediary emergentcontent item being within a degree of similarity to the end state of thefirst content item provides continuity between the intermediary emergentcontent item and the end state of the first content item thereby makingthe intermediary emergent content item appear more realistic.

As represented by block 630 b, in some implementations, the end state ofthe intermediary emergent content item is within a degree of similarityto the initial state of the second content item. For example, asillustrated in FIG. 2F, the end state 220 d of the intermediary emergentcontent item 220 matches (e.g., is identical to) the initial state 156of the second content item 150. In various implementations, the endstate of the intermediary emergent content item being within a degree ofsimilarity to the initial state of the second content item providescontinuity between the intermediary emergent content item and theinitial state of the second content item thereby making the intermediaryemergent content item appear more realistic.

As represented by block 630 c, in some implementations, a third state ofthe objective-effectuator at the beginning of the intermediary timeduration is within a degree of similarity to the first state of theobjective-effectuator at the end of the first time duration. Forexample, as illustrated in FIG. 2C, a state of the boyobjective-effectuator 222 at time t2,0 matches (e.g., is identical to) astate of the boy action-performing element 202 at time t1,n.

As represented by block 630 d, in some implementations, a fourth stateof the objective-effectuator at the end of the intermediary timeduration is within a degree of similarity to the second state of theobjective-effectuator at the beginning of the second time duration. Forexample, as illustrated in FIG. 2F, a state of the boyobjective-effectuator 222 at time t2,n matches a state of the boyaction-performing element 202 at time t3,0.

As represented by block 630 e, in some implementations, the set ofactions indicate a transition of the objective-effectuator from thefirst state at the end of the first time duration to the second state atthe beginning of the second time duration. For example, FIGS. 2C-2Findicate a transition of the boy objective-effectuator 222 from itsstate at the end of the first time duration T1 to its state at thebeginning of the second time duration T3.

As represented by block 630 f, in some implementations, theobjective-effectuator is absent in the first content item and present inthe second content item, and the set of actions correspond to anentrance of the objective-effectuator into the second content item.

As represented by block 630 g, in some implementations, theobjective-effectuator is present in the first content item and absent inthe second content item, and the set of actions correspond to adeparture of the objective-effectuator from the first content item. Forexample, FIGS. 2C-2F illustrate a departure of the girlobjective-effectuator 224.

As represented by block 640, in some implementations, the end state ofthe first content item indicates scene information characterizing afirst scene included within the first content item and the initial stateof the second content item indicates scene information characterizing asecond scene included within the second content item.

As represented by block 642, in some implementations, synthesizing theintermediary emergent content item includes synthesizing a third scenebased on the scene information characterizing the first scene and thescene information characterizing the second scene.

As represented by block 644, in some implementations, the first scenecorresponds to a first geographical location, the second scenecorresponds to a second geographical location, and the third scenecorresponds to a third geographical location that is on a route thatspans between the first geographical location and the secondgeographical location.

As represented by block 610 a, in some implementations, the method 600includes performing scene analysis on the first content item in order toidentify the objective-effectuator and determine the first state of theobjective-effectuator. In various implementations, performing sceneanalysis reduces the need for a user to manually specify the end stateof the first content item thereby reducing the number of userinteractions with the device and improving battery life.

As represented by block 620 a, in some implementations, the method 600includes performing scene analysis on the second content item in orderto identify the objective-effectuator and determine the second state ofthe objective-effectuator. In various implementations, performing sceneanalysis reduces the need for a user to manually specify the initialstate of the second content item thereby reducing the number of userinteractions with the device and improving battery life.

Referring to FIG. 6D, as represented by block 650, in someimplementations, the method 600 includes selecting a plot template froma plurality of plot templates, and synthesizing the intermediaryemergent content item based on the plot template. For example, asillustrated in FIG. 1, the emergent content engine 110 selects a plottemplate 162 from the plot template datastore 160, and the emergentcontent engine 110 utilizes the selected plot template 162 to generatethe objectives 112. In various implementations, synthesizing theintermediary emergent content item based on a plot template makes theintermediary emergent content item appear as realistic as the first andsecond content items thereby enhancing user experience and improving theoperability of the device.

As represented by block 652, in some implementations, the plot templateis selected based on the end state of the first content item and theinitial state of the second content item. For example, in someimplementations, the method 600 includes selecting the same plottemplate that is used by the first content item and the second contentitem.

As represented by block 654, in some implementations, selecting the plottemplate includes obtaining a user selection of the plot template (e.g.,via the plot affordances 552 shown in FIG. 5G). In variousimplementations, allowing the user to select the plot template gives theuser control over the plot/storyline of the intermediary emergentcontent item thereby enhancing user experience and improving theoperability of the device.

As represented by block 656, in some implementations, the method 600includes providing the plot template to the emergent content engine inorder to allow the emergent content engine to generate the set ofbounded objectives based on the plot template. For example, asillustrated in FIG. 2B, the interim objectives 210-218 are generatedbased on the plot template 162 a.

FIG. 7A is a flowchart representation of a method 700 of training anobjective-effectuator engine to generate actions that correspond to anintermediary emergent content item. In various implementations, themethod 700 is performed by a device with a non-transitory memory and oneor more processors coupled with the non-transitory memory (e.g., thedevice 900 shown in FIG. 9). In some implementations, the method 700 isperformed by processing logic, including hardware, firmware, software,or a combination thereof. In some implementations, the method 700 isperformed by a processor executing code stored in a non-transitorycomputer-readable medium (e.g., a memory). Briefly, in someimplementations, the method 700 includes extracting a set of actionsperformed by an action-performing element, determining a set ofobjectives for an objective-effectuator based on the set of actions, andtraining an objective-effectuator engine that generates actions for theobjective-effectuator.

As represented by block 710, in some implementations, the method 700includes extracting, from a content item, a set of actions performed byan action-performing element in the content item. For example, asillustrated in FIG. 3A, the action extractor 174 extracts the first setof actions 142 from the first content item 140.

As represented by block 720, in some implementations, the method 700includes determining, by semantic analysis, a set of objectives for anobjective-effectuator based on the set of actions. For example, asillustrated in FIG. 3A, the objective determiner 176 utilizes semanticanalysis to derive the first set of learned objectives 144 from theextracted actions 142. In some implementations, a synthesized reality(SR) representation of the objective-effectuator corresponds to theaction-performing element.

As represented by block 730, in some implementations, the method 700includes training, based on the set of objectives, anobjective-effectuator engine that generates actions for theobjective-effectuator. For example, as illustrated in FIG. 3A, thetrainer 130 trains the objective-effectuator engine 120 based on thefirst set of learned objectives 144. In some implementations, thetraining is complete when actions generated by the objective-effectuatorengine are within an acceptability threshold of the set of actionsextracted from the content item. For example, as illustrated in FIG. 3A,the training of the objective-effectuator engine 120 is complete whenthe generated actions 122 are within an acceptability threshold of theextracted actions 142. In some implementations, the training is completewhen the actions generated by the objective-effectuator engine arewithin a degree of similarity to (e.g., identical to) the set of actionsextracted from the content item.

In various implementations, training the objective-effectuator engineallows the objective-effectuator engine to generate actions thatcorrespond to an intermediary emergent content item thereby providingthe user with more content to watch. Providing the user more content towatch enhances the user experience and improves the operability of thedevice. Enabling the objective-effectuator engine to generate actionsthat corresponds to an intermediary emergent content item is lessresource-intensive than a content creator curating content. Hence,training the objective-effectuator engine tends to conserve computingresources.

As represented by block 710 a, in some implementations, theaction-performing element performs actions that advance a plot in thecontent item. In some implementations, the action-performing element isa character or an equipment (e.g., the boy action-performing element 202shown in FIG. 2A).

As represented by block 710 b, in some implementations, the method 700includes performing scene analysis on the content item in order toidentify the action-performing element and extract the set of actionsthat the action-performing element performs in the content item. Forexample, as shown in FIG. 3A, the action extractor 174 performs sceneanalysis on the first content item 140 and extracts the first set ofactions 142 from the first content item 140.

As represented by block 720 a, in some implementations, the SRrepresentation includes an augmented reality (AR) representation. Insome implementations, the SR representation includes a virtual reality(VR) representation. In some implementations, the SR representationincludes a mixed reality (MR) representation.

As represented by block 730 a, in some implementations, the method 700includes determining that the training is complete when actionsgenerated by the objective-effectuator engine are within a degree ofsimilarity to the actions extracted from the content item. For example,as shown in FIG. 3A, the training of the objective-effectuator engine120 is complete when the trainer 130 determines that the generatedactions 122 match the extracted actions 142.

As represented by block 730 b, in some implementations, the method 700includes determining values of one or more parameters of theobjective-effectuator engine. For example, as shown in FIG. 3A, thetrainer 130 determines the parameters 132 for the objective-effectuatorengine 120.

As represented by block 730 c, in some implementations, the method 700includes comparing the actions generated by the objective-effectuatorengine with the set of actions extracted from the content item, andadjusting the values of the one or more parameters based on thecomparison. For example, as shown in FIG. 3A, the action comparator 134compares the generated actions 122 with the extracted actions 142, andthe parameter determiner 136 adjusts the parameters 132 based on adifference between the generated actions 122 and the extracted actions142.

As represented by block 730 d, in some implementations, an amount ofadjustment to the values of the one or more parameters is a function ofa degree of dissimilarity between the actions generated by theobjective-effectuator and the set of actions extracted from the contentitem. For example, as shown in FIG. 3A, the parameter determiner 136adjusts the parameters 132 based on the difference between the generatedactions 122 and the extracted actions 142.

Referring to FIG. 7B, as represented by block 740, in someimplementations, the method 700 includes extracting, from anothercontent item, another set of actions that the objective-effectuatorperforms in the other content item. In some implementations, the method700 includes determining another set of objectives based on the otherset of actions. In some implementations, the method 700 includes furthertraining the objective-effectuator engine based on the other set ofobjectives.

As represented by block 740 a, in some implementations, the method 700includes determining that the training is complete when theobjective-effectuator engine generates a third set of actions that arewithin a degree of similarity to the first set of actions and a fourthset of actions that are within a degree of similarity to the second setof actions.

In various implementations, utilizing multiple content items (e.g.,multiple episodes or an entire season of a show) to train anobjective-effectuator engine results more realistic intermediaryemergent content items thereby enhancing user experience and improvingthe operability of the device that generates the intermediary emergentcontent items.

As represented by block 750, in some implementations, the method 700includes training, based on the set of actions, an emergent contentengine that generates objectives for the objective-effectuator. Forexample, training the emergent content engine 110 shown in FIG. 1.

As represented by block 750 a, in some implementations, the method 700includes determining that the training of the emergent content engine iscomplete when the emergent content engine generates objectives thatmatch the first set of objectives determined based on the first set ofobjectives. For example, as shown in FIG. 1, the training of theemergent content engine 110 is complete when the objectives 112generated by the emergent content engine 110 match the first set oflearned objectives 144 and/or the second set of learned objectives 154.

As represented by block 760, in some implementations, the method 700includes extracting another set of actions that anotheraction-performing element performs in the content item, determininganother set of objectives based on the other set of actions, andtraining, based on the other set of objectives, anotherobjective-effectuator engine that generates actions for anotherobjective-effectuator that corresponds to the other action-performingelement.

As represented by block 770, in some implementations, the method 700includes determining a plot template that corresponds with the contentitem, and providing the plot template to the objective-effectuatorengine during the training. For example, as shown in FIG. 1, theemergent content engine 110 selects one of the plot templates 162 fromthe plot template datastore 160.

As represented by block 770 a, in some implementations, the method 700includes selecting the plot template from a plurality of plot templatesbased on the set of objectives from the objective-effectuator and/orbased on the set of actions performed by the action-performing elementin the content item. Selecting the plot template based on the set ofobjectives and/or the set of actions makes the intermediary emergentcontent item appear more realistic thereby enhancing user experience andimproving the effectiveness of the device that is synthesizing theintermediary emergent content item.

FIG. 8A is a flowchart representation of a method 800 of generating anintermediary emergent content item in accordance with someimplementations. In various implementations, the method 800 is performedby a device with a non-transitory memory and one or more processorscoupled with the non-transitory memory (e.g., the device 900 shown inFIG. 9). In some implementations, the method 800 is performed byprocessing logic, including hardware, firmware, software, or acombination thereof. In some implementations, the method 800 isperformed by a processor executing code stored in a non-transitorycomputer-readable medium (e.g., a memory). Briefly, in someimplementations, the method 800 includes displaying a user interfacethat includes representations of content items, obtaining a user inputcorresponding to a request to generate an intermediary emergent contentitem, and displaying a representation of the intermediary emergentcontent item.

As represented by block 810, in some implementations, the method 800includes displaying, on the display, a user interface that includes afirst representation of a first content item spanning a first timeduration and a second representation of a second content item spanning asecond time duration. For example, as shown in FIG. 5A, the userinterface 500 includes the first episode representation 510 and thesecond episode representation 512.

As represented by block 820, in some implementations, the method 800includes obtaining, via the input device, a user input corresponding toa request to generate an intermediary emergent content item spanningover an intermediary time duration that is between the end of the firsttime duration and the beginning of the second time duration. Forexample, as shown in FIG. 5B, the user input 530 a corresponds to arequest create gap content that spans an intermediary time duration thatis between the first episode and the second episode.

As represented by block 830, in some implementations, the method 800includes in response to obtaining the user input, displaying, on thedisplay, a representation of the intermediary emergent content itembetween the first representation of the first content item and thesecond representation of the second content item. In someimplementations, the intermediary emergent content item is synthesizedafter the user input is obtained. For example, as shown in FIG. 5E, thegap content representation 511 is displayed between the first episoderepresentation 510 and the second episode representation 512.

Referring to FIG. 8B, as represented by block 840, in someimplementations, in response to obtaining the user input, the method 800includes displaying a prompt that includes a first affordance thatcorresponds to generating a standard version of the intermediaryemergent content item and a second affordance that corresponds togenerating a customized version of the intermediary emergent contentitem. For example, as shown in FIG. 5D, the prompt 540 includes astandard generation affordance 542 and a customized generationaffordance 544. Displaying the prompt allows the user to generate astandard version of the intermediary emergent content item or acustomized version of the intermediary emergent content item therebyproviding more device functionality.

As represented by block 842, in some implementations, the method 800includes detecting a selection of the first affordance. In someimplementations, in response to detecting the selection of the firstaffordance corresponding to the standard version, the method 800includes synthesizing the standard version of the intermediary emergentcontent item without obtaining additional user inputs. For example, asshown in FIGS. 5D-5E, in response to receiving the user input 530 b, thedevice generates the gap content and displays the gap contentrepresentation 511.

As represented by block 844, in some implementations, the method 800includes detecting a selection of the second affordance. In someimplementations, in response to detecting the selection of the secondaffordance corresponding to the customized version, the method 800includes displaying, on the display, a customization screen that allowscustomization of the intermediary emergent content items. For example,as shown in FIGS. 5F-5G, in response to the user input 530 c, the devicedisplays the customization screen 550.

As represented by block 846, in some implementations, the customizationscreen includes a plurality of plot affordances that correspond torespective plot templates for the intermediary emergent content item.For example, as shown in FIG. 5G, the customization screen 550 includesthe plot affordances 552.

As represented by block 846 a, in some implementations, one or more ofthe plot affordances are not selectable based on an end state of thefirst content item and an initial state of the second content item. Forexample, as shown in FIG. 5H, some of the plot affordances 552 are notselectable.

As represented by block 848, in some implementations, the customizationscreen includes a plurality of location affordances that correspond torespective locations for the intermediary emergent content item. Forexample, as shown in FIG. 5G, the customization screen 550 includes thelocation affordances 554.

As represented by block 848 a, in some implementations, one of theplurality of location affordances corresponds to an end state of thefirst content item. For example, as shown in FIG. 5G, one of thelocation affordances 554 allows the user to select a location for thegap content that corresponds to the location where the first episodeends.

As represented by block 848 b, in some implementations, one of theplurality of location affordances corresponds to an initial state of thesecond content item. For example, as shown in FIG. 5G, one of thelocation affordances 554 allows the user to select a location for thegap content that corresponds to the location where the second episodebegins.

As represented by block 848 c, in some implementations, one of theplurality of location affordances includes an input field that accepts alocation for the intermediary emergent content item. For example, asshown in FIG. 5G, one of the location affordances 554 includes an inputfield that allows the user to specify a location that is different fromthe location where the first episode ends and the location where thesecond episode begins.

As represented by block 850, in some implementations, the customizationscreen includes plurality of affordances that correspond toaction-performing elements that can be included in the intermediaryemergent content item. For example, as shown in FIG. 5G, thecustomization screen 550 includes various action-performing elementaffordances 556.

As represented by block 850 a, in some implementations, one of theplurality of affordances corresponds to an action-performing elementfrom the first content item. For example, as shown in FIG. 5G, some ofthe action-performing element affordances 556 correspond toaction-performing elements from the first episode.

As represented by block 850 b, in some implementations, one of theplurality of affordances corresponds to an action-performing elementfrom the second content item. For example, as shown in FIG. 5G, some ofthe action-performing element affordances 556 correspond toaction-performing elements from the second episode.

As represented by block 850 c, in some implementations, one of theplurality of affordances corresponds to an action-performing elementthat was not present in the first content item and the second contentitem. For example, as shown in FIG. 5G, some of the action-performingelement affordances 556 correspond to action-performing elements thatare neither present in the first episode nor in the second episode.

Referring to FIG. 8C, as represented by block 860, in someimplementations, the customization screen includes a plurality of timeaffordances that correspond to respective time durations for theintermediary emergent content item. For example, as shown in FIG. 5G,the customization screen 550 includes the time affordances 558.

As represented by block 870, in some implementations, the representationof the intermediary emergent content item is associated with a shareaffordance that allows sharing the intermediary emergent content itemwith other devices. For example, as shown in Figure SI, the gap contentrepresentation 511 is associated with the sharing affordance 580.

As represented by block 880, in some implementations, the representationof the intermediary emergent content item is associated with a modifyaffordance that allows modifying the intermediary emergent content item.For example, as shown in FIG. 5I, the gap content representation 511 isassociated with the modification affordance 570.

As represented by block 880 a, in some implementations, the method 800includes detecting a selection of the modify affordance. In response todetecting the selection of the modify affordance, the method 800includes displaying a modification screen that allows modification of aplot template, a location, action-performing elements and a timeduration associated with the intermediary emergent content item. Forexample, as shown in FIGS. 5I-5J, in response to receiving the userinput 530 d, the modification screen 572 is displayed.

FIG. 9 is a block diagram of a device 900 in accordance with someimplementations. While certain specific features are illustrated, thoseof ordinary skill in the art will appreciate from the present disclosurethat various other features have not been illustrated for the sake ofbrevity, and so as not to obscure more pertinent aspects of theimplementations disclosed herein. To that end, as a non-limitingexample, in some implementations the device 900 includes one or moreprocessing units (CPUs) 901, a network interface 902, a programminginterface 903, a memory 904, and one or more communication buses 905 forinterconnecting these and various other components.

In some implementations, the network interface 902 is provided to, amongother uses, establish and maintain a metadata tunnel between a cloudhosted network management system and at least one private networkincluding one or more compliant devices. In some implementations, theone or more communication buses 905 include circuitry that interconnectsand controls communications between system components. The memory 904includes high-speed random access memory, such as DRAM, SRAM, DDR RAM orother random access solid state memory devices, and may includenon-volatile memory, such as one or more magnetic disk storage devices,optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. The memory 904 optionallyincludes one or more storage devices remotely located from the one ormore CPUs 901. The memory 904 comprises a non-transitory computerreadable storage medium.

In some implementations, the memory 904 or the non-transitory computerreadable storage medium of the memory 904 stores the following programs,modules and data structures, or a subset thereof including an optionaloperating system 906, the emergent content engine 110, theobjective-effectuator engines 120-1, . . . , 120-n, the plot templatedatastore 160 including the plot templates 162, and theobjective-effectuator trainer 130.

Referring to FIG. 10, an example operating environment 1000 includes acontroller 102 and a head-mountable device (HMD) 104. In the example ofFIG. 10, the HMD 104, being worn by a user 10, presents (e.g., displays)an SR setting according to various implementations. In the example ofFIG. 10, the SR setting corresponds to (e.g., displays) the intermediarycontent item 220. In some implementations, the HMD 104 includes anintegrated display (e.g., a built-in display) that displays the SRsetting. In some implementations, the HMD 104 includes a head-mountableenclosure. In various implementations, the head-mountable enclosureincludes an attachment region to which another device with a display canbe attached. For example, in some implementations, an electronic devicecan be attached to the head-mountable enclosure. In variousimplementations, the head-mountable enclosure is shaped to form areceptacle for receiving another device that includes a display (e.g.,the electronic device). For example, in some implementations, theelectronic device slides/snaps into or otherwise attaches to thehead-mountable enclosure. In some implementations, the display of thedevice attached to the head-mountable enclosure presents (e.g.,displays) the SR setting (e.g., the intermediary content item 220). Invarious implementations, examples of the electronic device includesmartphones, tablets, media players, laptops, etc. In someimplementations, the controller 102 and/or the HMD 104 include theemergent content engine 110 that generates the intermediary content item220.

While various aspects of implementations within the scope of theappended claims are described above, it should be apparent that thevarious features of implementations described above may be embodied in awide variety of forms and that any specific structure and/or functiondescribed above is merely illustrative. Based on the present disclosureone skilled in the art should appreciate that an aspect described hereinmay be implemented independently of any other aspects and that two ormore of these aspects may be combined in various ways. For example, anapparatus may be implemented and/or a method may be practiced using anynumber of the aspects set forth herein. In addition, such an apparatusmay be implemented and/or such a method may be practiced using otherstructure and/or functionality in addition to or other than one or moreof the aspects set forth herein.

It will also be understood that, although the terms “first”, “second”,etc. may be used herein to describe various elements, these elementsshould not be limited by these terms. These terms are only used todistinguish one element from another. For example, a first node could betermed a second node, and, similarly, a second node could be termed afirst node, which changing the meaning of the description, so long asall occurrences of the “first node” are renamed consistently and alloccurrences of the “second node” are renamed consistently. The firstnode and the second node are both nodes, but they are not the same node.

The terminology used herein is for the purpose of describing particularimplementations only and is not intended to be limiting of the claims.As used in the description of the implementations and the appendedclaims, the singular forms “a”, “an,”, and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “comprises” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

1-61. (canceled)
 62. A method comprising: at a device including anon-transitory memory and one or more processors coupled with thenon-transitory memory: obtaining an end state of a first content itemspanning a first time duration, wherein the end state of the firstcontent item indicates a first state of a synthesized reality (SR) agentat the end of the first time duration; obtaining an initial state of asecond content item spanning a second time duration subsequent the firsttime duration, wherein the initial state of the second content itemindicates a second state of the SR agent at the beginning of the secondtime duration; and synthesizing an intermediary emergent content itemspanning over an intermediary time duration that is between the end ofthe first time duration and the beginning of the second time duration,wherein synthesizing the intermediary content item includes: generatinga set of bounded objectives for the SR agent by providing the end stateof the first content item and the initial state of the second contentitem to an emergent content engine, wherein the set of boundedobjectives are bounded by the end state of the first content item andthe initial state of the second content item, generating a set ofactions for the SR agent by providing the set of bounded objectives toan SR agent engine, wherein the first action in the set of actionsmatches an action of the SR agent at the end of the first time durationand the last action in the set of actions matches an action of the SRagent at the beginning of the second time duration, and rendering theintermediary emergent content item for display.
 63. The method of claim62, wherein an initial state of the intermediary emergent content itemis within a degree of similarity to the end state of the first contentitem.
 64. The method of claim 62, wherein an end state of theintermediary emergent content item is within a degree of similarity tothe initial state of the second content item.
 65. The method of claim62, wherein a third state of the SR agent at the beginning of theintermediary time duration is within a degree of similarity to the firststate of the SR agent at the end of the first time duration.
 66. Themethod of claim 65, wherein a fourth state of the SR agent at the end ofthe intermediary time duration is within a degree of similarity to thesecond state of the SR agent at the beginning of the second timeduration.
 67. The method of claim 62, wherein the set of actionsindicate a transition of the SR agent from the first state at the end ofthe first time duration to the second state at the beginning of thesecond time duration.
 68. The method of claim 62, wherein the SR agentis absent in the first content item and present in the second contentitem, and the set of actions corresponds to an entrance of the SR agentinto the second content item.
 69. The method of claim 62, wherein the SRagent is present in the first content item and absent in the secondcontent item, and the set of actions corresponds to a departure of theSR agent from the first content item.
 70. The method of claim 62,wherein the end state of the first content item indicates sceneinformation characterizing a first scene included within the firstcontent item and the initial state of the second content item indicatesscene information characterizing a second scene included within thesecond content item.
 71. The method of claim 70, wherein synthesizingthe intermediary emergent content item includes synthesizing a thirdscene based on the scene information characterizing the first scene andthe scene information characterizing the second scene.
 72. The method ofclaim 71, wherein the first scene corresponds to a first geographicallocation, the second scene corresponds to a second geographicallocation, and the third scene corresponds to a third geographicallocation that is on a route that spans between the first geographicallocation and the second geographical location.
 73. The method of claim62, wherein obtaining the end state of the first content item comprises:performing scene analysis on the first content item in order to identifythe SR agent and determine the first state of the SR agent.
 74. Themethod of claim 62, wherein obtaining the initial state of the secondcontent item comprises: performing scene analysis on the second contentitem in order to identify the SR agent and determine the second state ofthe SR agent.
 75. The method of claim 62, wherein synthesizing theintermediary emergent content item comprises: selecting a plot templatefrom a plurality of plot templates; and synthesizing the intermediaryemergent content item based on the plot template.
 76. The method ofclaim 75, wherein the plot template is selected based on the end stateof the first content item and the initial state of the second contentitem.
 77. The method of claim 75, wherein selecting the plot templatecomprises: obtaining a user selection of the plot template.
 78. Themethod of claim 75, wherein generating the set of bounded objectivescomprises: providing the plot template to the emergent content engine inorder to allow the emergent content engine to generate the set ofbounded objectives based on the plot template.
 79. A device comprising:one or more processors; a non-transitory memory; and one or moreprograms stored in the non-transitory memory, which, when executed bythe one or more processors, cause the device to: obtain an end state ofa first content item spanning a first time duration, wherein the endstate of the first content item indicates a first state of a synthesizedreality (SR) agent at the end of the first time duration; obtain aninitial state of a second content item spanning a second time durationsubsequent the first time duration, wherein the initial state of thesecond content item indicates a second state of the SR agent at thebeginning of the second time duration; and synthesize an intermediaryemergent content item spanning over an intermediary time duration thatis between the end of the first time duration and the beginning of thesecond time duration, wherein synthesizing the intermediary content itemincludes: generate a set of bounded objectives for the SR agent byproviding the end state of the first content item and the initial stateof the second content item to an emergent content engine, wherein theset of bounded objectives are bounded by the end state of the firstcontent item and the initial state of the second content item, generatea set of actions for the SR agent by providing the set of boundedobjectives to an SR agent engine, wherein the first action in the set ofactions matches an action of the SR agent at the end of the first timeduration and the last action in the set of actions matches an action ofthe SR agent at the beginning of the second time duration, and renderthe intermediary emergent content item for display.
 80. The device ofclaim 79, wherein synthesizing the intermediary emergent content itemcomprises: selecting a plot template from a plurality of plot templates;and synthesizing the intermediary emergent content item based on theplot template.
 81. A non-transitory memory storing one or more programs,which, when executed by one or more processors of a device, cause thedevice to: obtain an end state of a first content item spanning a firsttime duration, wherein the end state of the first content item indicatesa first state of a synthesized reality (SR) agent at the end of thefirst time duration; obtain an initial state of a second content itemspanning a second time duration subsequent the first time duration,wherein the initial state of the second content item indicates a secondstate of the SR agent at the beginning of the second time duration; andsynthesize an intermediary emergent content item spanning over anintermediary time duration that is between the end of the first timeduration and the beginning of the second time duration, whereinsynthesizing the intermediary content item includes: generate a set ofbounded objectives for the SR agent by providing the end state of thefirst content item and the initial state of the second content item toan emergent content engine, wherein the set of bounded objectives arebounded by the end state of the first content item and the initial stateof the second content item, generate a set of actions for the SR agentby providing the set of bounded objectives to an SR agent engine,wherein the first action in the set of actions matches an action of theSR agent at the end of the first time duration and the last action inthe set of actions matches an action of the SR agent at the beginning ofthe second time duration, and render the intermediary emergent contentitem for display.